Impact of COVID on school closures

Dhivya

18/08/2020

Objective

## Objective :- 
##  
##  > To understand the school closures across the globe due to the pandemic. 
##  > To study if there is significant co-relation between School Closures and any of the following: 
##    * Enrolment in public schools 
##    * Income levels of the countries 
##    * Geographical impact 
##  > Understanding the impact of income levels on education also contribute to a major part of this analysis.
## Data Source :- 
##  
##  This is an open source dataset published in Kaggle. 
##  This data was provided by UNESCO and was captured from Jan-2020 to April 2020, across several countries. 
##  Illustration and observations were arrived based on the dataset on Education and COVID.
## Data Description :- 
##  
##  The Dataset contains information about the closure of schools around the globe such as status and date of closing. 
##  It also contains the No. of students enrolled in various levels of school around the globe in various countries. 
##  Figures correspond to the number of learners enrolled at pre-primary, primary, secondary as well as 
##  at tertiary education levels.

Dataset :- https://www.kaggle.com/landlord/education-and-covid19

Overview of School Closures

## Inference :- 
##  1. School Closures does not seem to be based on the geographic location. 
##  2. There is a mix of school closures across the different regions.

Global Level Analysis

## Inference 
##  1. Majority of the countries across the globe fall under the high-income category. 
##  2. Mean enrolments in Public schools across the globe :- 9615947 
##  3. Median of enrolments in Public schools across the globe :-  1885226 
##  4. Most of the countries have closed their schools due to the pandemic.

Region (Cotinent) Level Analysis

## Inference 
##  1. Most of the European countries fall under the high-income category. 
##  2. All the North American countries fall under the high-income category. 
##  3. The low-income countries are primarily the countries in Saharan Africa. 
##  4. The income levels in South Asia ranges closely between the upper-middle-income to low-income categories. 
##  5. The number of South Asian countries in the low-income & upper-middle-income are nearly the same.

Region (Cotinent) Level Analysis

## Inference 
##  1. Mean enrolment in schools is hightest in South Asia. This could be due to the outlier with maximum enrolments. 
##  2. The mean of enrolments in South Asia and North America are similar. 
##  However, the mean in the South Asian lower-middle-income category is significantly higher than North Americas. 
##  3. Europe, having significantly more number of countries in the high-income category have 
##  the least enrolment in schools across the globe. 
##  4. The high-income countries in the Saharan Africa have the lowest enrolment in schools.

Region (Cotinent) Level Analysis

## Inference 
##  1. Latin America tops the school closures due to the pandemic, followed by the countries in Saharan Africa. 
##  2. All the schools in South Asia were closed due to the pandemic. 
##  3. European schools, especially, from a considerable number of high-income countries, 
##  have remained open with limitations. 
##  Could this be an indicator that the high-income countries pursue a stronger economy?

Schools Closure based on timeline

## Inference 
##  1. School Closure started as early as January in East Asia, indicating the outbreak of COVID in China. 
##  2. School Closures were at a peak, in March 2020, synonymous with the lockdown. 
##  3. While most countries shut down their schools in March, several countries in Eurpoe and Central Asia,  
##  and quite a few countries in Saharan Africa have been operating with limitations.

Effect of Enrolments on School Cloure - Location wise

## Inference 
##  1. The size of the circles indicate the number of enrollments. 
##  2. With high enrolments in the South Asian regions and moderate enrolments in South American countries, 
##  schools have remained closed. 
##  3. African region have nominal enrolments & varied school closure statuses. 
##  4. European region have low to moderate enrolments and most schools have remained open 
##  (most of them, with limitations). 
##  5. Schools in East Asia with the highest enrolments have functioned with limitations.  
##  
##  Summary :- 
##  The number of enrolments in public schools does not seem to contibute to the closure of schools, 
##  irrespective of the schools' or students' access to online or other alternate methods of eductaion.

Effect of Income on School Cloure - Location wise

## Inference 
##  1. The size of the circles indicate the Income level. 
##  2. Schools in the lower-middle-income countries have been closed, though the enrolment count 
##  is highest in these countries. 
##  This is contradictory to the fact about the inherent 'digital divide' in the developing/under-developed countries. 
##  Data about the size and facilities of these schools may have to be analysed to understand 
##  if the schools were closed, because it was not possible to follow the said norms to contain the pandemic.  
##  3. Schools continued to be 'open with limitations' in the upper middle income & high income countries.  
##  Data on the economic health of these countries may have to be analysed, to derive at a conclusion. 
##  
##  Summary :- 
##  The income levels vs school closure is inconclusive.

Does income levels affect the enrolments?

## Inference 
##  1. Pre-Primary enrolments are lowest across all income levels. 
##  It is common to enrol kids in the school, directly in the Primary level. 
##  2. Starting from the Primary level, the number of enrolments have reduced in the subsequent school levels, 
##  across all the income categories. 
##  3. Enrolment across all the school levels are highest in the lower-middle-income category. 
##  
##  Summary :- 
##  1. Low income category can be excluded, owing to affordability to school education. 
##  2. Considering only the high-income, upper-middle-income & lower-middle-income categories, 
##  there is an 'inverse co-relation' between the income levels and the enrolment in schools. 
##  3. The dropouts at each school level also seem to have an 'inverse co-relation' with the income level.

Regions vs Enrolements vs Income levels vs School Closure

## Inference :- 
##  1. East Asian countries that fall under the upper-middle-income level have significantly more enrolments 
##  and schools functioning with limitations. 
##  
##  2.Except the East Asian countries, the enrolments are significantly lower in the other  
##  upper-middle-income countries. 
##  
##  3. Similarly, expect the North American countries, the rest of the countries in the high-income level  
##  have significantly lower enrolment in public schools. 
##  
##  4. Schools in the Saharan African countries, irrespective of the income levels were either 
##  open with limitations or closed only in selected areas. 
##  This could be an indicator of non-access to the digital or alternate methods of education 
##  or lesser impact of the pandemic. 
##  
##  5. All the schools, across all the South Asian countries and across all the income levels, have remained closed.

Summary

## 1. The enrolment in public schools is not directly related to the closure of schools. 
##  2. The enrolment in public schools seem to have an inverse-corelation with the income levels of the country. 
##  3. The relationship between income levels and school closure is inconclusive. 
##  Data on the economic health of the countries are to be studied to understand the underlying factors. 
##  4. There is no significant relationship between the geographic location and the school closures.

Dhivya Karthic

## Subject Matter Expert, Data Visualization (IIT Madras) 
##   Program Manager, CTM (Certificate Programme in Technology & Management) 
##     (CTM - a joint programme by IIT Madras & IIM Bangalore - https://ctm-iitm.iimbx.edu.in/)
https://www.linkedin.com/in/dhivyakarthic/